DK-Port: construction and validation of port autonomous driving simulation environment based on large language models and reinforcement learning by LOU Yunjie, AI Mingfei, ZHUANG Shujie, YU Hai, WANG Xin, TENG Chu, WANG Jiangcheng, SHEN Tianyu, HAO Kunkun, CUI Wen

“…Then, human-machine mixed-traffic scenarios are constructed using complex road data, and adversarial driver models are trained with the PPO reinforcement learning algorithm to identify safety vulnerabilities in autonomous driving systems. …”
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Machine learning-based approach for reduction of energy consumption in hybrid energy storage electric vehicle by T. Paulraj, Yeddula Pedda Obulesu

“…A Long Short-Term Memory (LSTM) neural network is trained using real-world drive cycle data and exported in Open Neural Network Exchange (ONNX) format for real-time deployment within a Simulink-based control environment. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…The responses served as features to train and test machine learning (ML) models. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. …”
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A neural network-based method for input parameter optimization of edge transport modeling utilizing experimental diagnostics by Y. Luo, S. Xu, Y. Liang, E. Wang, J. Cai, Y. Feng, D. Reiter, A. Knieps, S. Brezinsek, D. Harting, M. Krychowiak, D. Gradic, P. Ren, D. Zhang, Y. Gao, G. Fuchert, A. Pandey, M. Jakubowski, the W7-X Team

“…Subsequently, the trained surrogate model is incorporated into a Bayesian inference framework with Dynamic Nested Sampling to infer posterior distributions of the EMC3-EIRENE input parameters. …”
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Blockchain framework with IoT device using federated learning for sustainable healthcare systems by B. Bhasker, P. Muralidhara Rao, P. Saraswathi, S. Gopal Krishna Patro, Javed Khan Bhutto, Saiful Islam, Mohammed Kareemullah, Addisu Frinjo Emma

“…Federated learning (FL) addresses previous challenges using a centralized aggregate server to distribute global learning models. The local participant controls patient data, ensuring data confidentiality and security. …”
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Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall by Se-Dong Jang, Jae-Hwan Yoo, Yeon-Su Lee, Byunghyun Kim

“…By utilizing past rainfall data, 1D drainage system simulations, and 2D flood analyses, we trained the model to predict flood patterns for various rainfall events. …”
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A Study on the Performance Improvement of Automatic Intellectual Property Counseling Classification: Using the Transformer-based AI Model BERT by Dong-Hun Noh, Jae-Ok Min, So-Youn Woo

“…After fine-tuning the BERT model, which was pre-trained using patent counseling text data and professional counselor classification values data, it was observed that the BERT’s automatic classification distribution was more similar to that of professional counselors than the classification distribution of the existing TA. …”
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Generative priors-constraint accelerated iterative reconstruction for extremely sparse photoacoustic tomography boosted by mean-reverting diffusion model: Towards 8 projections by Teng Lian, Yichen Lv, Kangjun Guo, Zilong Li, Jiahong Li, Guijun Wang, Jiabin Lin, Yiyang Cao, Qiegen Liu, Xianlin Song

“…., mean state), a mean-reverting diffusion model is trained to learn prior information of the data distribution. …”
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Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model by Olivier Munyaneza, Jung Woo Sohn

“…This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. …”
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A User-Centric Smart Library System: IoT-Driven Environmental Monitoring and ML-Based Optimization with Future Fog–Cloud Architecture by Sarkan Mammadov, Enver Kucukkulahli

“…A key challenge is the lack of adaptive frameworks balancing objective sensor data with subjective user experience. This study introduces an Internet of Things (IoT)-powered framework integrating real-time sensor data, image-based occupancy tracking, and user feedback to enhance study conditions via machine learning (ML). …”
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Subsurface Cavity Imaging Based on UNET and Cross–Hole Radar Travel–Time Fingerprint Construction by Hui Cheng, Yonghui Zhao, Kunwei Feng

“…To address these limitations, this study proposes a deep learning–based imaging method that introduces the concept of travel–time fingerprints, which compress raw radar data into structured, low–dimensional inputs that retain key spatial features. …”
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Land cover classification for Siberia leveraging diverse global land cover datasets by Munseon Beak, Kazuhito Ichii, Yuhei Yamamoto, Ruci Wang, Beichen Zhang, Ram C. Sharma, Tetsuya Hiyama

“…The validations showed that: (a) the generated new land cover data achieved the highest overall accuracy (85.04%) and kappa coefficient (82.62%); (b) the classifications of mixed forest (user accuracy: 97.85%) and grasses (user accuracy: 94.85%) demonstrated improvements, showing higher performance compared to most other types; and (c) by comparing the distribution of land cover across climate zones, we discovered that temperature is a critical factor throughout Siberia. …”
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Enhancing satellite image compositing with temporal proximity weighting for deep learning–based cropland segmentation by Reza Maleki, Falin Wu, Guoxin Qu, Amel Oubara, Gongliu Yang

“…Generating composite images from satellite data is crucial for crop mapping over defined periods. …”
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A method to retrieve mixed-phase cloud vertical structure from airborne lidar by E. Crosbie, E. Crosbie, J. W. Hair, A. R. Nehrir, R. A. Ferrare, C. Hostetler, T. Shingler, D. Harper, M. Fenn, M. Fenn, J. Collins, J. Collins, R. Barton-Grimley, B. Collister, K. L. Thornhill, K. L. Thornhill, C. Voigt, C. Voigt, S. Kirschler, S. Kirschler, A. Sorooshian, A. Sorooshian

“…<p>A technique was developed to provide cloud phase information using data collected by the NASA Langley airborne High Spectral Resolution Lidar systems with a particular emphasis on mixed-phase cloud conditions, where boundaries and gradients in the distribution of ice and liquid water are critically important for microphysical and radiative processes. …”
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Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging by Yoshihiro Obata, Dilworth Y. Parkinson, Daniël M. Pelt, Claire Acevedo

“…Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. …”
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Spatial Proximity Relations-Driven Semantic Representation for Geospatial Entity Categories by Yongbin Tan, Hong Wang, Rongfeng Cai, Lingling Gao, Zhonghai Yu, Xin Li

“…Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge and help achieve the deep fusion of geospatial data. …”
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Machine learning-based smart irrigation controller for runoff minimization in turfgrass irrigation by Sambandh Dhal, Jorge Alvarado, Ulisses Braga-Neto, Benjamin Wherley

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Generative and Contrastive Self-Supervised Learning for Virulence Factor Identification Based on Protein–Protein Interaction Networks by Yalin Yao, Hao Chen, Jianxin Wang, Yeru Wang

“…Moreover, a severe imbalance exists between virulence and non-virulence proteins, which causes existing models trained on balanced datasets by sampling to fail in incorporating proteins’ inherent distributional characteristics, thus restricting generalization to real-world imbalanced data. …”
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An Artificial Intelligence Model for Sensing Affective Valence and Arousal from Facial Images by Hiroki Nomiya, Koh Shimokawa, Shushi Namba, Masaki Osumi, Wataru Sato

“…We constructed a graphical user interface to show real-time affective valence and arousal states by analyzing facial video data. Our model is the first distributable AI model for sensing affective valence and arousal from facial images/videos to be developed based on an empirical database; we anticipate that it will have many practical uses, such as in mental health monitoring and marketing research.…”
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Assessment of improved neonatal ward infrastructure on neonatal health outcomes in southern Malawi by Ingunn Haraldsdóttir, Bob Milanzi Faque, Thordur Thorkelsson, Geir Gunnlaugsson

“… # Conclusions Neonatal survival improved with more space, better-trained staff and upgrade of equipment. Monitoring of admitted newborns and their clinical care and data management and storage was a significant problem, alongside staff shortage. …”
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